Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification

被引:215
作者
He, Nanjun [1 ,2 ,3 ]
Paoletti, Mercedes E. [3 ]
Mario Haut, Juan [3 ]
Fang, Leyuan [1 ,2 ]
Li, Shutao [1 ,2 ]
Plaza, Antonio [3 ]
Plaza, Javier [3 ]
机构
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
[2] Key Lab Visual Percept & Artificial Intelligence, Changsha 410082, Hunan, Peoples R China
[3] Univ Extremadura, Dept Technol Comp & Commun, Escuela Politecn, Hyperspectral Comp Lab, Caceres 1003, Spain
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2019年 / 57卷 / 02期
关键词
Data augmentation; deep convolutional neural networks (CNNs); hyperspectral image (HIS) classification; multiscale covariance maps (MCMs); SPECTRAL-SPATIAL CLASSIFICATION; EXTINCTION PROFILES; NEURAL-NETWORKS; IMPLEMENTATION; CNN;
D O I
10.1109/TGRS.2018.2860464
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a result, exploiting both spatial and spectral information is very important for HSI classification. This paper proposes a new handcrafted feature extraction method, based on multiscale covariance maps (MCMs), that is specifically aimed at improving the classification of HSIs using CNNs. The proposed method has the following distinctive advantages. First, with the use of covariance maps, the spatial and spectral information of the HSI can be jointly exploited. Each entry in the covariance map stands for the covariance between two different spectral bands within a local spatial window, which can absorb and integrate the two kinds of information (spatial and spectral) in a natural way. Second, by means of our multiscale strategy, each sample can be enhanced with spatial information from different scales, increasing the information conveyed by training samples significantly. To verify the effectiveness of our proposed method, we conduct comprehensive experiments on three widely used hyperspectral data sets, using a classical 2-D CNN (2DCNN) model. Our experimental results demonstrate that the proposed method can indeed increase the robustness of the CNN model. Moreover, the proposed MCMs+2DCNN method exhibits better classification performance than other CNN-based classification strategies and several standard techniques for spectral-spatial classification of HSIs.
引用
收藏
页码:755 / 769
页数:15
相关论文
共 55 条
[1]  
[Anonymous], 2018, J REAL-TIME IMAGE PR
[2]  
[Anonymous], 2015, DEEP LEARNING NATURE, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
[3]  
[Anonymous], IEEE T GEOSCI REMOTE
[4]  
[Anonymous], IEEE T GEOSCI REMOTE
[5]  
[Anonymous], ISPRS J PHOTOGRAMM R
[6]   Classification of hyperspectral data from urban areas based on extended morphological profiles [J].
Benediktsson, JA ;
Palmason, JA ;
Sveinsson, JR .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (03) :480-491
[7]   Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network [J].
Cao, Xiangyong ;
Zhou, Feng ;
Xu, Lin ;
Meng, Deyu ;
Xu, Zongben ;
Paisley, John .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 27 (05) :2354-2367
[8]   Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks [J].
Chen, Yushi ;
Jiang, Hanlu ;
Li, Chunyang ;
Jia, Xiuping ;
Ghamisi, Pedram .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (10) :6232-6251
[9]   Deep Learning-Based Classification of Hyperspectral Data [J].
Chen, Yushi ;
Lin, Zhouhan ;
Zhao, Xing ;
Wang, Gang ;
Gu, Yanfeng .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) :2094-2107
[10]   Morphological Attribute Profiles for the Analysis of Very High Resolution Images [J].
Dalla Mura, Mauro ;
Benediktsson, Jon Atli ;
Waske, Bjoern ;
Bruzzone, Lorenzo .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2010, 48 (10) :3747-3762